Opportuni)es and Challenges in e-Governance:

mHealth Case Study

Vishnu Pendyala

A Great Quote

Economy grows as more and more people join its core echelons. People are the most important economic resources at all )mes.

Agenda

The Healthcare Challenge and the e-Governance Opportunity.

Mo)va)on, Enabling Factors mHealth: An Overview Vision: Machine Augmented Mindfulness Medical Diagnosis Realizing the Vision: Challenges Future Direc)ons and Conclusion

Questions for you Why is technical research important for

eec)ve e-governance? How is mHealth related to Machine Learning,

Big Data Analy)cs and Internet of Things? How can the millions who do not have access to

proper healthcare be provisioned with it? Can machines replace the doctor eventually? Can machine assisted gene therapy become a

cure-all? What are some of the opportuni)es and

challenges in the mHealth space?

Source: World Health Organization

The Healthcare Challenge: World Map of Access to Physicians

Copyright Sasi Group (University of Sheffield) and Mark Newman (University of Michigan).

Healthcare Spending

Source: World Health Organization, World Health Statistics, 2010: Table 7

Propor)onal Mortality Rate in India

Source: World Health Organization

E-Governance in Healthcare Governance is all about inclusion, provisioning and common good control is a small part.

Work mostly has been in automa)ng processes and dissemina)ng informa)on to masses.

Just one MMP in NeGP: mcts. Governments have the power to inuence and provision the research needs.

Need for soWware applica)ons in health that can reach the masses and revolu)onize care.

What are some such applica)ons?

Mo)va)on and Enabling Factors Millions of underprivileged who do not have access to healthcare.

Automated diagnosis has been a formidable challenge for the past 4 decades.

Prolifera)on of portable compu)ng devices, wearables.

Informa)on Retrieval has come of age: Self-Diagnosis on the Internet is common.

Cloud Compu)ng gives ubiquitous access to enormous processing power.

Gartners Hype Cycle 2014

Source: Gartner

The Reach of Mobile Devices in Developing Countries

mHealth: An Overview Use of mobile devices for various aspects of healthcare is part of eHealth, called mHealth.

From Telemedicine to awareness campaigns. Connect pa)ents, community health workers and physicians to serve at the point of care.

Phone as a point-of-care device: devices such as ultrasound probe plugged into the phone.

Scores of projects already func)oning successfully, par)cularly in India.

Machine Augmented Mindfulness Mind monitors health, diagnoses condi)ons and even cures at a molecular level.

Harvard studies established that mindfulness creates a feedback loop to govern the health.

Even deadly diseases such as cancer are found to be caused by the imbalances in the mind.

Can machines take-on mindfulness, like they took over reasoning, compu)ng and thinking?

Machine Augmented Mindfulness: Current State

Machines have successfully diagnosed diseases. Molecular level cure is possible via gene therapy that is a_rac)ng huge investments.

Wearables make it possible to constantly monitor the state of health.

Mind cannot be replaced, but some of its func)onality can be replicated.

We just put all these pieces of the puzzle together to unfold the vision!

The Vision: E-Governance in Healthcare

What happens in the cloud is the Research Focus

Government owns the cloud and the processes in it

Medical diagnosis is just one example of what happens in the cloud

Approaches to Medical Diagnosis Conven)onal: First-Order Logic based reasoning; Rule-bases in olden days; Seman)c Web now.

MYCIN from Stanford had 600 rules and did be_er than Medical Experts.

Latest Trend: Using Machine Learning and Informa)on Retrieval.

None of the solu)ons so far are general purpose and suitable for mass deployment.

Our approach considers it as a problem in Text Mining: Given a set of discharge sheets, iden)fy the one thats closest to the given symptoms.

Guidelines Need a solu)on for medical diagnosis that Is based on available data: past diagnoses Is general, inexpensive, and adequate enough to be used by the masses

Can be implemented using current technologies for faster availability

Does not need a whole lot of knowledge engineering or ongoing expert maintenance.

A Sample Discharge Sheet Diagnosis: Allergic Bronchitis with Asthma Case Summary: Patient 36 years male was admitted with complaints of breathlessness & cough for last 7 days. At the time of admission Pulse 126/min, BP 130/90 mmHg, RR 24/min, SpO2 94 with O2, Chest spasms wheezing+, ronchi++. Patient was investigated & treated conservatively with I/V antibiotics, I/V fluids, Nebulization & other supportive treatment. Now the patient is being discharged in satisfactory condition. Treatment Advice: * Tab. Augmentin 1 gm 1 tab. twice daily * Syp. Rapitus 2 TSF thrice daily * Tab. Deriphyllin-R 150 mg 1 tab. twice daily * Forocort Rotacap 1 cap. Twice daily with Rotahaler

Wordcloud from the Discharge Sheets

Text mining the Corpus

Preprocessing: Remove stopwords, numbers, punctua)on, tags, sparse terms and convert case.

Each document is represented as a vector (a point) in a mul)-dimensional space.

Each dimension is a word in the corpus => thousands of dimensions.

Each document has a score (TF-IDF) for each word used in it that determines its posi)on.

Each point is labeled with the diagnosis.

3D Visualiza)on of the Corpus

Enter: The Pa)ent

The closer the points in the vector space, the more similar the documents.

Document with pa)ents symptoms are also represented as a vector in the same vector space.

The label on the closest point to this symptoms document in the vector space is the diagnosis.

Find K-Nearest Neighbors (K-NN) of the symptoms document to suggest possible alterna)ves.

The K nearest neighboring discharge sheets and the symptoms doc can be used to nd K relevant ar)cles for further reference and decision support.

Simple Math with Profound Impact

TF.IDF Score is computed as: !.idft,d = !t,d * idft where Similarity between two discharge sheets is

where the norm of a vector, | | is being the m.idf value of a feature (term / word), i

Challenges

Privacy and Security: Hacking can be fatal. Cost: Technology is s)ll in commercializa)on zone. Dataset: Government mandate may be needed. Human Exper)se: Interven)on needed at )mes. Quality of available dataset. Mul)lingual support: 22 ocial languages, 1,652 dierent "mother tongues" in India alone.

High Cost of Type I and II errors. Acceptance: Skep)cism government can again help here to inuence posi)ve outlook.

Future Direc)ons

Processing images, video, and audio (including ultrasound) in conjunc)on with text informa)on.

Real-)me, stateful big data processing. Aggrega)on of health data to detect or predict epidemics and health trends.

Add a QA interface using NLP, IVR, Machine Transla)on.

Extend the ideas to monitor and proac)vely remedy abnormali)es.

Conclusion

ICT for Healthcare is a formidable challenge and a huge opportunity for e-governance.

Mobile is the www of 90s the conduit to take solu)ons to the masses and manifold the RoI.

Good )me to revisit 70s ideas we now have the compu)ng resources that werent there then.

And more importantly

You can be a Grand Winner too

Source: http://www.npr.org/blogs/goatsandsoda/2014/09/26/351515298/and-the-million-dollar-hult-prize-goes-to-a-doc-in-a-box

Even more importantly

A Great Quote

Math is the heart of ma_er. Once expressed in math, the ma_er dissolves and yields, just like when you touch a persons heart, he dissolves and yields.

Questions for you Why is technical research important for

eec)ve e-governance? How is mHealth related to Machine Learning,

Big Data Analy)cs and Internet of Things? How can the millions who do not have access to

proper healthcare be provisioned with it? Can machines replace the doctor eventually? Can machine assisted gene therapy become a

cure-all? What are some of the opportuni)es and

challenges in the mHealth space?

Resources h_p://www.ecgcheck.com/ h_p://www.alivecor.com/home h_p://www.mobisante.com h_p://www.technologyreview.com/news/421827/ultrasound-gets-more-portable/ h_ps://www.google.com/search?q=%22Mobile%20Alliance%20for%20Maternal%20Ac)on h_p://en.wikipedia.org/wiki/Scanadu h_p://en.wikipedia.org/wiki/Lab-on-a-chip h_p://nrhm-mcts.nic.in

References

[1] Pendyala, V.S., Fang, Y., Holliday, J., Zalzala, A., (2014, October). A Text Mining Approach to Automated Healthcare for the Masses. In Global Humanitarian Technology Conference (GHTC), 2014, IEEE Interna)onal Conference on. IEEE. [2] Pendyala, V.S., & Shim, S. S. (2009). The Web as the Ubiquitous Computer. IEEE Computer, 42(9) 90-92. [3] Pendyala, V.S., & Holliday, J. (2010, August). Performing intelligent mobile searches in the cloud using seman)c technologies. In Granular Compu)ng (GrC), 2010 IEEE Interna)onal Conference on (pp. 381-386). IEEE. [4] Pendyala, V.S., & Holliday, J. (2011). Cloud As a Computer. Advanced Design Approaches to Emerging SoWware Systems:: Principles, Methodology and Tools, 241. [5] Delizonna, L. L., Williams, R. P., & Langer, E. J. (2009). The eect of mindfulness on heart rate control. Journal of Adult Development, 16(2), 61-65.

References (Contd.) [6] Anderson, C. M. (2000). From molecules to mindfulness: How ver)cally convergent fractal )me uctua)ons unify cogni)on and emo)on*. Consciousness & Emo)on, 1(2), 193-226. [7] Amritanshuram, R., Nagendra, H. R., Shastry, A. S. N., Raghuram, N. V., & Nagarathna, R. (2013). A psycho-oncological model of cancer according to ancient texts of yoga. Journal of Yoga and Physical Therapies, 3, 129. [8] Reibel, D. K., Greeson, J. M., Brainard, G. C., & Rosenzweig, S. (2001). Mindfulness-based stress reduc)on and health-related quality of life in a heterogeneous pa)ent popula)on. General hospital psychiatry, 23(4), 183-192. [9] Chandwani, K. D., Chaoul-Reich, A., Biegler, K. A., & Cohen, L. (2008). MindBody Research in Cancer. In Integra)ve Oncology (pp. 139-160). Humana Press. [10] Ludwig, D. S., & Kabat-Zinn, J. (2008). Mindfulness in medicine. Jama, 300(11), 1350-1352.

Q&A

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